Deepfakes and Reputation Risk…

The
probability that businesses – higher profile public figures will experience…the adverse effect of deepfake’s represents a
formidable reputation risk. For most humans, businesses, and notables, the
‘deepfake’ risk manifests as an inability to distinguish images – video which
have been technologically altered. As of last week (sic), the ‘news consuming
public’, for the most part, presume what they see – hear is the original
reality. In today’s fractious socio – economic and political environment,
what constitutes – how one defines original reality is likely to be
influenced by – filtered through one’s political leanings.

Numerous
celebrities and politicians have already experienced the consequences…of the simpler – 1.0 versions of deepfakes
software which businesses would be obliged to recognize – predict what’s will
likely be on the horizon. In light of the burgeoning phenomenon of deepfakes, the
probability – inevitability of their occurrence should not be construed or
manifest as dismissiveness or ‘wait and see’!
Far too much at stake.

Conceptually
and practically, as I understand it, GANS were (variously – commercially)
developed…in their present form,
and introduced (open source) in mid-2014by Ian Goodfellow and Yoshua Bengio, among other
researchers, at the University of Montreal.
There are numerous inferences – assumptions about other origins of GANS, i.e., the technology and code to produce present
day deepfakes.

It’s not a
role I wish to pursue here – now, to either give credence to or dispute…such inferences, as that information, i.e.,
assigning responsibility or fault, seems somewhat irrelevant at this point.
Deepfakes are here to stay, with little, if any probability of turning the
clock backwards, regulating their use, or placing limitations on their
(adverse) capabilities.

Admittedly,
I am aware of very few positives that may arise from deepfake software…aside from specific U.S. intelligence
initiatives, presumably ‘new age art’, or as a tool to liven parties and family
gatherings, i.e., a ‘technologized game of charades’. Of course, various forms
of what could legitimately be considered deepfakes have been variously utilized
in film since the late 1980’s by the likes of Steven Spielberg and George
Lucas, et al.

Can the application of deepfakes become a life-threatening
(dangerous) offensive weapon… perhaps not, but their
application can, as we have already witnessed in numerous instances…

of course, there are adverse costs,
but, as-yet, they are largely incalculable because deepfake video, audio,
images ‘look and sound like the real thing’.

The actualization of deepfakes, presents
additional layers of challenge to reputation risk… mitigation
when compared to more conventional (non-deepfake) reputation risks. We can
presume reputation risks born by deepfakes, will likely be more costly and time
consuming to try to reverse the inevitable adverse narrative that
follows.

Some victims of deepfakes presume the prudent
– most effective tool to mitigate…deepfake born reputation
risk, be it video, audio, image, etc., is to…

try to dominate and counter the
subsequent adverse narrative through conventional strategies – methods, ala
some sort of fact checking, etc., or

identify and out the economic –
competitive advantage – political – special interest adversary which funded –
promoted the deepfake.

Still, it’s worth noting again, most
reputation risk mitigation should include understanding how difficult it can be…for
humans to reverse a perception – opinion that is tethered to an experienced reality. Afterall, that’s what deepfakes, by design, are
intended to achieve.

To the chagrin of those already adversely
affected…and others, i.e., humans, businesses, and
institutions, etc. which are likely to be adversely affected by deepfakes at
some point, most anyone can download deepfake software today can create variously
convincing – fake products in their spare time in the proverbial
basement. It’s just not rocket

Forward looking reputation risk (mitigation)
professionals…will have considered – factored the ease which
deepfakes can be produced and publicly emerge, e.g., (a.) a fake national
security – emergency (alert) warning that an attack by an adversary is imminent,
(b.) producing a deepfake that targets a political candidate timed to
‘go public’ only days before voters go to the polls, or, (c.) on a
different spectrum, malicious attempts to provoke adverse reactions against a political
adversary or business executives’ marriage – family relationships by producing
– publicizing a deepfake extra-marital liaison or sexual orientation.

Tim Hwang, director of the Ethics and
Governance of Artificial Intelligence Initiative at the Berkman-Klein Center
and MIT Media Lab…told CSO (chief security officer) Magazine
recently, “I think that certainly the demonstrations (of deepfakes) that
we’ve seen are disturbing and I think they are concerning, and they raise a lot
of questions, but I’m skeptical they change the game in a way which a lot of
people are suggesting.” Respectfully,
I do not wholly agree with Dr. Hwang’s perspective.

Through my lens, a broader, worrisome, and
problematic challenge underlying deepfakes…has to do with the
‘human reality’ that (1.) seeing – hearing is believing, and (2.) one’s
truth lies in believing what is seen and/or heard, to which, most psychologists
– psychiatrists concur…

humans are innately inclined to seek
information, perspective, and speech which supports a pre-existing view point
and/or what they want to believe, and

are likely to ignore – dismiss opposing
– differing perspectives.

be
it about the legitimacy of, or, conspiracy theories related to the existence –
presence of UFO’s (unidentified flying objects), man landing – walking on the
moon’s surface, the Loch Ness monster, ‘big foot’, if Speaker of the U.S. House
of Representatives’ Nancy Pelosi actually slurred her speech during a televised
Q&A at a national conference, or whether the body (head to toe) in a
photograph actually ‘belongs’ to the person it claims.

Arguably, either human inclination, can be
vulnerable to…technological alterations of reality, ala
deepfakes. Less arguably, those inclined
to use (deepfake) technologies in a malicious manner can (a.) quickly
dominate the ‘always on’ news cycles, and (b.) acquiresignificant
power to wrongfully influence opinion through deliberate (technologically
manufactured) falsehoods which…

spread at keystroke speed

under the guise of truthful
representations of reality.

Any presumption that a business can (just
as quickly) counter – reverse fake…machinations through (conventional)
well publicized pronouncements of fakery and fact checking, are likely to be
subordinate to the human phenomenon of ‘seeing – hearing is believing’ which
there are countless (sad, tragic) reminders ala PizzaGate, Sand Hook
Elementary School, and the conspiracy theorists underlying much of the daily
content delivered at InfoWars, etc.

Deepfakes exploit these human tendencies
by…using
GANS (generative adversarial networks) in which two ‘opposite
thinking – doing’ ML (machine learning) models. One model focuses ‘its
attention’ on specific data sets and then creates image-video forgeries; meanwhile,
the other (opposing) model focuses its attention on specific aspects of that
image-video which has – is being developed to detect the presence of fake –
forged features.

The so-called forgery model continues to
create – generate fake features until…the opposing model is
less able to detect – distinguish the forgery from the original reality.
The larger the data set from which the forgery model extracts – applies
relevant features, it becomes less challenging for the forgery model to create
a ‘believable’ (undetectable – undistinguishable) deepfake.

This is a primary reason why images – videos
of former presidents and celebrities, etc…have frequently been
targets of the still early, first generation of deepfake (software), i.e., there is an abundance of publicly available
(open source) video footage and/or images to ‘train’ either model.

the controversy surrounding the now
proven doctored video of President Trump’s confrontation with CNN reporter Jim
Acosta at a November 2018 (White House) press conference, makes clear. In this
instance, the real – original video clearly shows a White House intern
attempting to take the microphone from Acosta. Subsequent editing of the video
(presumably with authorization from within the White House) made it appear that
the CNN reporter physically pushed the intern away from her position to grasp
the microphone.

This very public incident should
accentuate concerns that…video can be relatively easily
manipulated to discredit most any target of choice, be it a reporter,
politician, business, institution, executive, or a specific brand.

However, unlike the more technologically
sophisticated products of deepfakes…wherein machine learning
software can ‘puts words in people’s mouths’, low-tech doctored video can and
not infrequently is close enough to representing a reality, that it blurs most
conventional lines between (distinguishing) what’s true from what’s patently
false.

The potential for GANS is perhaps infinite…in no small part, because these ‘robot
artists’ can learn (be taught) to mimic the distribution of designated data to
create images, music, speech, and/or prose, which is virtually
indistinguishable from our individual realities.

For example, in the context of reputation
risk, should an image – video, etc., suddenly emerge…of a corporate executive ‘appearing’ to be
engaged in (a.) acts – behaviors – speech contrary to business
principles, mission, or relevant law, or (b.) unfavorable to an existing
product, service, or client, , but, (c.) subsequently determined to be
deepfake…

surely
doesn’t require substantial imagination to recognize how this very probable
scenario could have immediate and adverse impact on investors, consumers, and vendors,

followed
by cascading effects that reverse – adversely alter future orders, sales,
R&D, marketing, and certainly business image and reputation.

An important component to mitigating
(vulnerability, probability, and criticality of) reputation risk…in the AI (artificial intelligence – machine
learning) era, is understanding GANS. Reputation risk practitioners are obliged
to know how ‘the basics’ how generative algorithms work, and for that,
contrasting them with discriminative algorithms is also instructive.
Discriminative algorithms try to classify input data; that is, given the
features of an instance of data, they predict a label or category to which that
data belongs.